By Alessandra Soto
In the Latino/Hispanic Community, many move to the United States to find financial independence and stability. Due to prioritizing socioeconomic status there is a creation of tunnel vision when migrating, which lacks planning for issues beyond finance. Therefore, the focus of this article is analyzing cognitive performance, specifically related to the Latino/Hispanic Community.Current research doesn’t focus on these issues in the Latino/Hispanic community, therefore there is a limited number of studies. Looking into other studies that focus on the majorities, such as White Americans, there can be a generalized idea of how that can be applied to other ethnicities or races. Despite this, researching different factors within different communities based on their internal state and environment can indicate fluctuations in cognitive performance. These differences are crucial because they can serve as meaningful indicators of real-world function and future trajectory (Weizenbaum et al., 2020).
Cognitive performance refers to how well the brain works when it comes to an individual's memory, attention, language skills, problem solving, and learning (Harvey, 2019). It is important for the Latino/Hispanic Community to be aware of how important it is to focus on their brain health. Not only is this for their well-being, but also for the purpose of achieving their goals after migration. The Latino/Hispanic community is known as a collective culture, therefore remembering birthdays, doing tasks for others, and saving money are big parts of their lives; which all rely on their cognitive performance... Additionally, many work long hours and hard jobs (housekeeping, landscaping, construction, etc…), therefore having a healthy brain and healthy body is vital for their survival. There are many factors that can affect cognitive performance such as stress management and staying mentally active, but one factor that is currently building up research is how sleep can affect cognitive performance.
Many have experienced how sleep can make us feel. For instance, when we sleep short hours before working a long shift, it can negatively affect our overall performance. However, a brief article I came across grabbed my attention. The article, “Too Much Sleep Increases Cognitive Decline in Latino Communities,” discusses how sleeping over 9 hours can increase cognitive decline. According to Dr. Ramos, the study they conducted identified that longer sleep correlates to neurocognitive decline that can later on develop dementia. This provided a target of early intervention to prevent the disease in Latinos, who are identified as a high-risk group (2020).
Reading this article, I was surprised to discover a new finding affecting cognitive performance. At first, it made sense that little sleep can affect overall performance, whereas longer hours of sleep just tend to happen when you’re super tired.
Another study that was analyzed., thoroughly examined connections between genetic factors influencing various aspects of sleep and cognitive aging outcomes in a sizable Hispanic/Latino population (Zhang et al.). Their findings indicate that genetic factors related to insomnia, daytime sleepiness, and sleep duration are linked to an increased risk of mild cognitive impairment (MCI). They also observed that there is an interaction effect between genetic predisposition and self-reported sleep duration on MCI risk (2022).
This topic also highlights how the combination of different factors can impact one thing, such as genetics, age, and sleep quality and quantity.. According to Hokett & Duarte, insufficient sleep has been linked to poorer memory outcomes in minoritized racial and ethnic groups. However, there are other factors many minority groups face that can impact their quality of life and sleep, such as racism-related stress and social determinants of health (2024).
Focusing on the study conducted by Ramos et al., the methods were broken up into population, neurocognitive function and score analysis, main exposures: questionnaires, cardiovascular risk factors, covariates, and statistical analyses. This cohort study gathered an initial sample of 16,145 Hispanic/Latinos aged 18-74 years. They were based in four US field centers: Chicago, IL; Miami, FL; Bronx, NY; San Diego, CA. Their method was to look at basic SDB, daytime sleepiness, insomnia, and sleep duration. SDB refers to sleep-disordered breathing, the symptoms include snoring and witnessed apnea. They wanted to see how these factors affected changes in episodic learning and memory, language skills, processing speed, and cognitive impairment. The study included different Hipanics/Latinos from Central America, Cuba, Dominican Republic, Mexican, Puerto Rico, and others from South America that are living in the United States. This was a 7 year study where they used questionnaires, tests, and self reports in order to pick up results. This all included their sleep time and behaviors, their verbal learning and memory, mental status, and intelligence (2020).
The baseline evaluation gathered each participant's information on sleep disorders, demographics, socioeconomic status, lifestyle habits (smoking, physical activities), medical history, and biological measures (anthropometrics, blood draw, oral glucose tolerance tests, among others).
They continued with an ancillary study designed to examine the prevalence and determinants of neurocognitive decline and disorders. They introduced new neuropsychological tests. This new test was conducted by being a multistage sampling strategy with stratification and clustering, and probability weights that account for nonresponse and attrition. The authors of this research conducted a systematic review with literature on PubMed using MeSH terms, “sleep”, “cognition”, and “prospective studies.” They reviewed 146 studies and focused on four relevant studies for focus. Based on the previous studies they reviewed, they interpreted for their own conducting study that important sleep correlates of neurocognitive decline. The studies had different populations and focused, therefore they redirected and added quantifying the role of sleep disorders and cerebrovascular disease as a pathway to neurocognitive decline. Perform genetic studies that can clarify casual associations with neurocognitive decline.
The baseline exams included the Six-Item Screener (SIS; mental status), Spanish English Verbal Learning Test (SEVLT; verbal episodic learning and memory); Controlled Oral Word Association (word/verbal fluency) Test of the Multilingual Aphasia Examination; and Digit Symbol Subtest (DSS; processing speed) of the Wechsler Adult Intelligence Scale-Revised. For them to evaluate cognitive decline, they also repeated the neurocognitive battery when the participants came for the second visit and administered the Trail Making Test parts A and B. The questionnaires focus on sleep habits that happen constantly during bedtime, wake time, and nap time. The questions asked were: What time do you usually go to bed? What time do you usually wake up? And average sleep duration? They broke down the sleep duration from short (<6 hours), intermediate (6-9 hours), and long (>9 hours). The scale they used and translated to the Spanish Version for their targeted participants, which was the Epworth Sleepiness Scale (ESS). This scale assesses whether the individual is likely to fall asleep based on eight common situations. Information about SDB was collected using ARES Unicorder 5.2 The sleep records were scored at the Sleep Reading Center. For the Cardiovascular risk factors, they used the Global Vascular Risk Score (GVRS) and individual risk factors. This score is used to predict 10-year risk of a stroke and other related events, the information collected includes ethnicity/race, waist circumference, health behaviors, and peripheral vascular disease. Depending on the scores, if the GRVS value of 8.2 indicated a 10% chance for remaining free of stroke, and infarction, or any other vascular impact over a 10-year period.
The covariates include age (measured at baseline), time lapse between sleep assessment and second visit measured in days, depressive symptoms using the Center for Epidemiologic Studies Depression Scale, and self-reports for sleep medication before the study was conducted.
Based on the table, the analysis and statistical outcomes were provided by using z-scores for the cognitive outcomes in order to do a comparison of the estimated associations across tests. Based on the cognitive performance at SOL-INCA was examined using survey linear regression models to independently test the associations between each sleep risk factor and standardized cognitive outcomes in both cross-sectional and prospective analyses. For each outcome, two regression models were fitted. The first model tested sex, age, and education and the time lapse between sleep assessment and second visit, while the second model, used baseline GVRS, depression symptoms, reported frequency of sleep medication, and field center. Change scores for repeated cognitive tests were then calculated using survey linear regression to predict cognitive performance, adjusted for age, education, and the lapsed time between cognitive assessments.
After the study was completed, the next aim was to analyze the experimental data to find patterns and other results away from the primary objective. Analysis of variance-based contrasts was used to test differences between sleep risk groups, providing estimates of marginal differences and their 95% confidence intervals for cognitive performance and cognitive change.
As highlighted by several researchers in their articles, there is an emphasis on research on minority groups. The Hispanic/Latino community face many of their own issues as immigrants compared to the predominant groups in the United States. Many within this community do not speak English, leading to a significant barrier in accessing and sharing information. It is crucial to raise awareness and address the existing barriers in obtaining information within this community. For instance , when analyzing research about different ethnic and racial groups, there is going to be a long list of factors that can affect the outcome. Some examples include cultural beliefs about aging and cognitive health, how researchers can use their effective communication strategies for minority groups to be informed, and even the different stigmatizations existing within ethnic or racial groups. This means there needs to be an understanding that seeking medical help is a sign of strength, not weakness. Also, resources in Spanish such as pamphlets and videos are a necessity so that patients can immediately understand what is happening. These are short and direct solutions that take a short amount of time and yield quick results.. However, as there is more research on this topic there can be a more collective understanding on issues involving the brain in the Latino/Hispanic community and better change initiatives.. Cognitive performance can be affected by many things, but all individuals will have their own extent of how it can be resolved, therefore further research is important.
References:
Harvey P. D. (2019). Domains of cognition and their assessment . Dialogues in clinical neuroscience, 21(3), 227–237. https://doi.org/10.31887/DCNS.2019.21.3/pharvey
Hokett, E., Duarte, A. A Review of Racial and Ethnic Differences in Sleep-memory Associations and the Potential Contributions of Social Determinants of Sleep. Curr Sleep Medicine Rep 10, 62–69 (2024). https://doi.org/10.1007/s40675-024-00281-0
Ramos W, Wu B, Redline S, et al. Sleep and neurocognitive decline in the Hispanic Community Health Study/Study of Latinos. Alzheimer's Dement. 2020; 16: 305–315. https://doi.org/10.1016/j.jalz.2019.08.191
Zhang, Y., Elgart, M., Granot-Hershkovitz, E., Wang, H., Tarraf, W., Ramos, A. R., Stickel, A. M., Zeng, D., Garcia, T. P., Testai, F. D., Wassertheil-Smoller, S., Isasi, C. R., Daviglus, M. L., Kaplan, R., Fornage, M., DeCarli, C., Redline, S., González, H. M., & Sofer, T. (2023). Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos. EBioMedicine, 87, 104393. https://doi.org/10.1016/j.ebiom.2022.104393
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